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Lind S. Gee

Publications and source records attributed to Lind S. Gee.

8 recordsLinked to original sources
USGS2018

Revised technical implementation plan for the ShakeAlert system—An earthquake early warning system for the West Coast of the United States

The U.S. Geological Survey (USGS), along with partner organizations, has developed an earthquake early warning (EEW) system called ShakeAlert for the highest risk areas of the United States: namely, California, Oregon, and Washington. The purpose of the system is to reduce the impact of earthquakes and save lives and property by providing alerts to institutional users and the public. Using networks of ground-motion sensors and sophisticated computer algorithms, ShakeAlert can detect an earthquake seconds after it begins, calculate its location and magnitude, and estimate the resulting intensity of shaking. Alerts can then be sent to people and systems that may experience damaging shaking, allowing them to take appropriate protective actions. Depending on the user’s distance from the earthquake, alerts may be delivered before, during, or after the arrival of strong shaking. ShakeAlert is built on the foundation of the sensor networks and data processing infrastructure of the USGS-led Advanced National Seismic System. However, these networks were not originally designed for EEW; old equip­ment needs to be updated and new stations must be added to construct EEW-capable networks. The ShakeAlert data-processing infrastructure includes redundant servers that are geographically distributed at monitoring centers in Seattle, Washington, as well as Menlo Park, Berkeley, and Pasadena in California. Three data-processing layers collect raw ground-motion data from field stations (data layer), analyze these data to estimate the area and intensity of the resulting shaking (production layer), and publish alert products as appropriate for end users (alert layer). The alert layer can support thousands of institutional users and alert redistributors, but the USGS does not have the mission, infrastructure, or expertise to perform public notifications and is therefore recruiting technology enablers from the private sector. Additionally, ShakeAlert will coordinate with both public and private partners to accomplish consistent and ongoing public communication, education, and outreach. The estimated cost of completing the ShakeAlert infrastructure and sensor networks is \$39.4 million and has an estimated annual operation and maintenance cost of \$28.6 million per year. Building a highly reliable data telemetry infrastructure would cost another \$20.5 million and operating this telemetry system would add \$49.8 million per year; however, these costs could be reduced if project partners provide bandwidth on existing systems.

Open-File Report↗
USGS2015

The data quality analyzer: a quality control program for seismic data

The U.S. Geological Survey's Albuquerque Seismological Laboratory (ASL) has several initiatives underway to enhance and track the quality of data produced from ASL seismic stations and to improve communication about data problems to the user community. The Data Quality Analyzer (DQA) is one such development and is designed to characterize seismic station data quality in a quantitative and automated manner. The DQA consists of a metric calculator, a PostgreSQL database, and a Web interface: The metric calculator, SEEDscan, is a Java application that reads and processes miniSEED data and generates metrics based on a configuration file. SEEDscan compares hashes of metadata and data to detect changes in either and performs subsequent recalculations as needed. This ensures that the metric values are up to date and accurate. SEEDscan can be run as a scheduled task or on demand. The PostgreSQL database acts as a central hub where metric values and limited station descriptions are stored at the channel level with one-day granularity. The Web interface dynamically loads station data from the database and allows the user to make requests for time periods of interest, review specific networks and stations, plot metrics as a function of time, and adjust the contribution of various metrics to the overall quality grade of the station. The quantification of data quality is based on the evaluation of various metrics (e.g., timing quality, daily noise levels relative to long-term noise models, and comparisons between broadband data and event synthetics). Users may select which metrics contribute to the assessment and those metrics are aggregated into a “grade” for each station. The DQA is being actively used for station diagnostics and evaluation based on the completed metrics (availability, gap count, timing quality, deviation from a global noise model, deviation from a station noise model, coherence between co-located sensors, and comparison between broadband data and synthetics for earthquakes) on stations in the Global Seismographic Network and Advanced National Seismic System.

Computers & Geosciences↗
USGS2014

Uncertainty estimates in broadband seismometer sensitivities using microseisms

The midband sensitivity of a seismic instrument is one of the fundamental parameters used in published station metadata. Any errors in this value can compromise amplitude estimates in otherwise high-quality data. To estimate an upper bound in the uncertainty of the midband sensitivity for modern broadband instruments, we compare daily microseism (4- to 8-s period) amplitude ratios between the vertical components of colocated broadband sensors across the IRIS/USGS (network code IU) seismic network. We find that the mean of the 145,972 daily ratios used between 2002 and 2013 is 0.9895 with a standard deviation of 0.0231. This suggests that the ratio between instruments shows a small bias and considerable scatter. We also find that these ratios follow a standard normal distribution ( R 2  = 0.95442), which suggests that the midband sensitivity of an instrument has an error of no greater than ±6 % with a 99 % confidence interval. This gives an upper bound on the precision to which we know the sensitivity of a fielded instrument.

Journal of Seismology↗
USGS2013

Obtaining changes in calibration-coil to seismometer output constants using sine waves

The midband sensitivity of a broadband seismometer is one of the most commonly used parameters from station metadata. Thus, it is critical for station operators to robustly estimate this quantity with a high degree of accuracy. We develop an in situ method for estimating changes in sensitivity using sine‐wave calibrations, assuming the calibration coil and its drive are stable over time and temperature. This approach has been used in the past for passive instruments (e.g., geophones) but has not been applied, to our knowledge, to derive sensitivities of modern force‐feedback broadband seismometers. We are able to detect changes in sensitivity to well within 1%, and our method is capable of detecting these sensitivity changes using any frequency of sine calibration within the passband of the instrument.

Bulletin of the Seismological Society of America↗
USGS2013

Seismic Station Installation Orientation Errors at ANSS and IRIS/USGS Stations

Many seismological studies depend on the published orientations of sensitive axes of seismic instruments relative to north (e.g., Li et al., 2011). For example, studies of the anisotropic structure of the Earth’s mantle through SKS‐splitting measurements (Long et al., 2009), constraints on core–mantle electromagnetic coupling from torsional normal‐mode measurements (Dumberry and Mound, 2008), and models of three‐dimensional (3D) velocity variations from surface waves (Ekström et al., 1997) rely on accurate sensor orientation. Unfortunately, numerous results indicate that this critical parameter is often subject to significant error (Laske, 1995; Laske and Masters, 1996; Yoshizawa et al., 1999; Schulte‐Pelkum et al., 2001; Larson and Ekström, 2002). For the Advanced National Seismic System (ANSS; ANSS Technical Integration Committee, 2002), the Global Seismographic Network (GSN; Butler et al., 2004), and many other networks, sensor orientation is typically determined by a field engineer during installation. Successful emplacement of a seismic instrument requires identifying true north, transferring a reference line, and measuring the orientation of the instrument relative to the reference line. Such an exercise is simple in theory, but there are many complications in practice. There are four commonly used methods for determining true north at the ANSS and GSN stations operated by the USGS Albuquerque Seismological Laboratory (ASL), including gyroscopic, astronomical, Global Positioning System (GPS), and magnetic field techniques. A particular method is selected based on site conditions (above ground, below ground, availability of astronomical observations, and so on) and in the case of gyroscopic methods, export restrictions. Once a north line has been determined, it must be translated to the sensor location. For installations in mines or deep vaults, this step can include tracking angles through the one or more turns in the access tunnel leading to the vault (e.g., GSN station WCI in Wyandotte Cave, Indiana). Finally, the third source of error comes from the ability of field engineers to orient the sensor relative to the reference line. In order to quantify bounds on the errors in each step in the orientation process, we conducted a series of tests at the ASL using twelve GSN and ANSS field engineers. The results from this exercise allow us to estimate upper bounds on the precision of our ability to orient instruments, as well as identify the sources of error in the procedures. We are also able to identify systematic bias of various true‐north‐finding methods relative to one another. Although we are unable to estimate the absolute accuracy of our orientation measurements due to our inability to identify true north without some error, the agreement between independent methods for finding true north provides confidence in the different approaches, assuming no systematic bias. Finally, our study neglects orientation errors that are beyond the control of the field engineer during a station visit. These additional errors can arise from deviations in the sensitive axes of the instruments relative to the case markings, processing errors (Holcomb, 2002) when comparing horizontal orientations relative to other sensors (e.g., borehole installations), and deviations of the sensitive axes of instruments from true orthogonality (e.g., instruments with separate modules such as the Streckeisen STS‐1).

Seismological Research Letters↗
USGS2009

U.S. Geological Survey Global Seismographic Network - Five-Year Plan 2006-2010

The Global Seismographic Network provides data for earthquake alerting, tsunami warning, nuclear treaty verification, and Earth science research. The system consists of nearly 150 permanent digital stations, distributed across the globe, connected by a modern telecommunications network. It serves as a multi-use scientific facility and societal resource for monitoring, research, and education, by providing nearly uniform, worldwide monitoring of the Earth. The network was developed and is operated through a partnership among the National Science Foundation (http://www.nsf.gov), the Incorporated Research Institutions for Seismology (http://www.iris.edu/hq/programs/gsn), and the U.S. Geological Survey (http://earthquake.usgs.gov/gsn).

Open-File Report↗